1. Field of the Invention
The present invention relates generally to the detection and diagnosis of tissue pathology using a diagnostic medical image, and, more particularly, to a method and apparatus for detecting and diagnosing the presence of pulmonary tissue pathology from CT images using an automated texture analysis procedure.
2. Description of the Related Art
Pulmonary emphysema is a common, debilitating, and progressive disorder of the lungs that may result from smoking. The disorder is caused by destruction of the alveolar walls of the lung parenchyma (i.e., lung tissue), which results in an abnormal enlargement of air spaces distal to the terminal bronchiole. Enlargement of these air spaces in the lungs impedes the exchange of oxygen in the air for carbon dioxide in the bloodstream. As a result of this impeded process, an individual experiences breathlessness, making ordinary tasks, once thought simple, labor intensive.
While emphysema causes tissue in the lungs to atrophy, other pulmonary diseases, such as idiopathic pulmonary fibrosis (IPF) and sarcoidosis (sarcoid), cause the build-up of tissue in the lungs. Albeit the effects of emphysema and IPF and sarcoid might seem to be directly opposite from one another, IPF and sarcoid also suffer the same negative symptom of chronic fatigue. That is, IPF and sarcoid also impede the carriage of oxygen from the lungs to the bloodstream like emphysema.
In addition to those pulmonary parenchymal diseases discussed above, peripheral small airways diseases, such as cystic fibrosis and asthma (along with over one-hundred other pathologies) also exist, which can adversely affect the lungs of an individual as well.
The debilitating effects of these pulmonary diseases are progressive and often permanent. Therefore, accurate diagnosis of these disorders at their earliest stage is extremely critical so that measures can be taken to thwart their advancement before significant damage occurs.
Pulmonary function tests have been conventionally used to indicate the presence of pulmonary diseases. However, these tests are not always able to properly distinguish between the abnormalities of the lung that result from one particular disorder from another.
Chest radiographs (i.e., X-ray projections) have also been used for diagnosing pulmonary diseases. However, because of problems resulting from structural superposition associated with projection images and inter and intra-observer variability in analysis, visual examination of these X-ray derived images are moderately reliable when a particular disease is well developed, and are effectively unreliable for identifying mild to moderate stages of the disease. Furthermore, external factors such as film speed, X-ray beam voltage, anode heel effect, and variations in chest wall thickness may adversely impact the radiographic density of the X-ray. Thus, the diagnosis of pulmonary disorders based upon radiographic density has proven to be unreliable as a result of these external factors.
X-ray computed tomography (CT), using X-ray energy, has proven to be more sensitive in demonstrating lung pathology, and, thus, more reliable than chest X-ray projection imaging in detecting pathological changes in lung tissue that are indicative of pulmonary diseases. CT""s greatest contribution is its ability to provide a view of the anatomy without interference from overlying and underlying structures within the body cavity. Tomographic imaging (from multiple energy sources) are proving to provide complimentary information. Although X-ray CT is currently the preferred imaging method for evaluating the lung, use of high concentration oxygen and hyperpolarized gases, such as helium and xenon, have made it possible to begin thinking about nuclear magnetic resonance imaging for use in the assessment of lung parenchymal and peripheral pathology.
While X-ray computed tomography provides advancement over the chest radiograph in visually depicting the characteristics of pulmonary diseases, diagnosis of these diseases has remained dependent upon the subjectivity of the trained observer (e.g., radiologist) who reads the CT image. The trained observers"" visual perception of different textures present on the CT images can be highly subjective, and thus, variations in accuracy is common between the trained observers. Furthermore, visual assessments provide limited sensitivity to small textural changes on the CT image. Thus, an early case of the pulmonary disorders may go undetected due to the physical limitations of the human eye, and the capacity of the brain to interpret the data. This would pose a serious disadvantage to the welfare of the patient, especially since the debilitating effects of these pulmonary diseases are often irreversible.
The present invention is directed to overcoming, or at least reducing the effects of, one or more of the problems set forth above.
In one aspect of the present invention, a method is provided for the automated analysis of textural differences present on a diagnostic medical image. The image comprises a plurality of pixels, with each pixel having a particular gray level assigned thereto. The method includes defining a region of interest (ROI) on the image; performing one or more first order texture measures within the ROI to describe a frequency of occurrence of all gray levels assigned to pixels of the ROI; performing one or more second order texture measures within the ROI to describe spatial interdependencies between particular pixels of the ROI; and classifying the ROI as belonging to a particular tissue pathology class based upon the first and second order texture measures obtained.
In another aspect of the present invention, an apparatus is provided for analyzing diagnostic medical images. The apparatus comprises an image input, which is adapted to receive a diagnostic medical image. The diagnostic medical image includes a plurality of pixels, with each pixel having a particular gray level assigned thereto. The apparatus further comprises a processor adapted to perform texture measures on a group of pixels within the image. The texture measures describe information on an occurrence frequency of gray levels assigned to a group of pixels and spatial interdependencies between particular pixels of the group of pixels. The processor is further adapted to classify the group of pixels to a particular tissue pathology class based upon the texture measures obtained.